Uncertainty Estimator-Based Sliding Mode Control For a Novel Spherical Robot With Cable Transmission Mechanism

This paper presents a new spherical robot with a cable transmission mechanism. Cable transmission mechanism replaces conventional gear train to eliminate the influence of gear backlash, lower the costs on mechanical customization, and can be arranged flexibly. By projection method, the 3D robot dynamic model with structural asymmetry is decoupled into balance subsystem and velocity subsystem, and the kinetics equations are established based on Newton-Euler's law. To estimate the unknown structural dynamics in the balance subsystem and external disturbance in the velocity subsystem, adaptive law containing both control and estimation error information is proposed for the uncertainty estimator (UE) design. Then, an uncertainty estimator-based sliding mode controller (UESMC) is introduced for balance and velocity control, leading to enhanced disturbance rejection capability and a reduced steady-state error. Simulations and experiments on a real spherical robot are conducted to demonstrate the efficacy of the proposed control strategies.

Stabilization of a Tractor with n Trailers in the Presence of Wheel Slip Effects

SUMMARY The purpose of this paper is to design a stabilizing controller for a car with n connected trailers. The proposed control algorithm is constructed on the Lyapunov theory. In this paper, the purpose of navigating the system toward the desired point considering the slip phenomenon as a main source of uncertainty is analyzed. First mathematical models are presented. Then, a stabilizing control approach based on the Lyapunov theory is presented. Subsequently, an uncertainty estimator is taken into account to overcome the wheel slip effects. Obtained results show the convergence properties of the proposed control algorithm against the slip phenomenon.

A brisk local uncertainty estimator for hydrologic simulations and predictions (Blue Cat)

<p>We propose a brisk method for uncertainty estimation in hydrology which maximizes the probabilistic efficiency of the estimated confidence bands over the whole range of the predicted variables. It is an innovative approach framed within the blueprint we proposed in 2012 for stochastic physically-based modelling of hydrological systems. We present the theoretical foundation which proves that global uncertainty can be estimated with an integrated approach by tallying the empirical joint distribution of predictions and predictands in the calibration phase. We also theoretically prove the capability of the method to correct the bias and to fit heteroscedastic uncertainty for any probability distribution of the modelled variable. The method allows the incorporation of physical understanding of the modelled process along with its sources of uncertainty. We present an application to a toy case to prove the capability of the method to correct the bias and the entire distribution function of the predicting model. We also present a case study of a real world catchment. We prepare open source software to allow reproducibility of the results and replicability to other catchments. We term the new approach with the acronym BLUE CAT: Brisk Local Uncertainty Estimation by Conditioning And Tallying.</p>

Reliability Analysis of Structures by Iterative Improved Ensemble of Surrogate Method

Surrogate models have been widely adopted for reliability analysis. The common approach is to construct a series of surrogates based on a training set and then pick out the best one with the highest accuracy as an approximation of the time-consuming limit state function. However, the traditional method increases the risk of adopting an inappropriate model and does not take full advantage of the data devoted to constructing different surrogates. Furthermore, obtaining more samples is very expensive and sometimes even impossible. Therefore, to save the cost of constructing the surrogate and improve the prediction accuracy, an ensemble strategy is proposed in this paper for efficiently analyzing the structural reliability. The values of the weights are obtained by a recursive process and the leave-one-out technique, in which the values are updated in each iteration until a given prediction accuracy is achieved. Besides, a learning function is used to guide the selection of the next sampling candidate. Because the learning function utilizes the uncertainty estimator of the surrogate to guide the design of experiments (DoE), to accurately calculate the uncertainty estimator of the ensemble of surrogates, the concept of weighted mean square error is proposed. After the high-quality ensemble of surrogates of the limit state function is available, the Monte Carlo method is employed to calculate the failure probabilities. The proposed method is evaluated by three analytic problems and one engineering problem. The results show that the proposed ensemble of surrogates has better prediction accuracy and robustness than the stand-alone surrogates and the existing ensemble techniques.